Keras to Kubernetes

Introduction

Welcome! This book introduces the topics of Machine Learning (ML) and Deep Learning (DL) from a practical perspective. I try to explain the basics of how these techniques work and the core algorithms involved. The main focus is on building real‐world systems using these techniques. I see many ML and DL books cover the algorithms extensively but not always show a clear path to deploying these algorithms into production systems. Also, we often see a big gap in understanding around how these Artificial Intelligence (AI) systems can be scaled to handle large volume of data—also referred to as Big Data.

Today we have systems like Docker and Kubernetes that help us package our code and seamlessly deploy to large on‐premise or Cloud systems. Kubernetes takes care of all the low‐level infrastructure concerns like scaling, fail‐over, load balancing, networking, storage, security, etc. I show how your ML and DL projects can take advantage of the rich features that Kubernetes provides. I focus on deployment of the ML and DL algorithms at scale and tips to handle large volumes of data.

I talk about many popular algorithms and show how you can build systems using them. I include code examples that are heavily commented so you can easily follow and possibly reproduce the examples. I use an example of a DL model to read images and classify logos of popular brands. Then this model is deployed on a distributed cluster so it can handle large volumes of client requests. This example ...